Análise - Doutorado - Edvaldo Aldo Litos Paulo Nhanombe

Pré-tratamento dos dados

dados_batata_v <- readxl::read_xlsx("../data-raw/Exp. Verão-2024_25.xlsx") |> 
  janitor::clean_names()
readr::write_rds(dados_batata_v,"../data/batata-doce-ednaldo-v.rds")

dados_multi_v <- readxl::read_xlsx("../data-raw/HTP_Verão_epocas_final.xlsx") |> 
  janitor::clean_names()
readr::write_rds(dados_multi_v,"../data/batata-doce-multiespectral-v.rds")

dados_batata_i <- readxl::read_xlsx("../data-raw/Exp. Inverno-2024_25.xlsx") |> 
  janitor::clean_names()
readr::write_rds(dados_batata_i,"../data/batata-doce-ednaldo-i.rds")

dados_multi_i <- readxl::read_xlsx("../data-raw/HTP_inverno_epocas_final.xlsx") |> 
  janitor::clean_names()
readr::write_rds(dados_multi_i,"../data/batata-doce-multiespectral-i.rds")

Análise exploratória - Batata Doce - INVERNO

Carregando pacotes e banco de dados

library(tidyverse)
data_set <- read_rds("../data/batata-doce-ednaldo-i.rds")
lista_variaveis <- data_set |> select(pt:tmspa_percent) |> names()
# map(lista_variaveis, ~{
#   data_set |> 
#     ggplot(aes(x=!!sym(.x), y = ..density..)) +
#     geom_histogram(color="black",fill="gray", bins = 15) +
#     labs(title = .x) +
#     theme_bw()
# })

Análise de resíduos - Pré-supostos da ANOVA

map(lista_variaveis, ~{
  print("========================")
  print(.x)
  print("========================")
  y <- data_set |> pull(!!sym(.x))
  trat <- data_set |> pull(designacao) |> as_factor()
  bloco <- data_set |> pull(bloco) |> as_factor()
  mod <- aov(y ~ trat + bloco)
  print(anova(mod))
  rs <- rstudent(mod)
  yp <- predict(mod)
  sw_test <- shapiro.test(rs)
  sw_test <- round(sw_test$p.value,5)
  print(
    as_tibble(rs) |> 
      ggplot(aes(rs)) +
      geom_histogram(bins=14,color="black",fill="aquamarine4") +
      labs(title = .x,
           subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
      theme_bw()
  )
  df_aux <- data_set |> 
    select(designacao, bloco,!!sym(.x)) |> 
    add_column(rs,yp)  |> 
    filter(rs > 3 | rs < -3)
  # arrange(rs)
  if(nrow(df_aux) != 0) print(df_aux)
  levene_teste <- lawstat::levene.test(y,trat)
  levene_teste <- round(levene_teste$p.value,5)
  box_plot <- data_set |> 
    group_by(designacao) |> 
    mutate(
      y_mean = median(!!sym(.x),na.rm=TRUE),
      designacao = as_factor(designacao))  |>
    ungroup() |> 
    mutate(designacao = designacao |>  fct_reorder(y_mean)) |> 
    ggplot(aes(x=as_factor(designacao),y=!!sym(.x),
               fill=as_factor(designacao))) +
    geom_boxplot() +
    scale_fill_viridis_d(option = "magma") +
    theme_bw()+
    labs(x="Designacao",
         title =  paste("Levene test - p-valor: ",levene_teste))
  
  print(
    box_plot
  )
  print(cat("\n"))
})
#> [1] "========================"
#> [1] "pt"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 9.1572 0.53866  3.2652 0.001617 **
#> bloco      2 0.7532 0.37658  2.2827 0.117427   
#> Residuals 34 5.6090 0.16497                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco    pt    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     1 2.52   3.25  1.60
#> 2         14     3 0.531 -3.09  1.42

#> 
#> NULL
#> [1] "========================"
#> [1] "pc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 9.2504 0.54414  3.8296 0.0004222 ***
#> bloco      2 0.7344 0.36720  2.5843 0.0901973 .  
#> Residuals 34 4.8310 0.14209                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco    pc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     1 2.50   3.91  1.52
#> 2         14     3 0.481 -3.72  1.43

#> 
#> NULL
#> [1] "========================"
#> [1] "pnc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.28610 0.016829  0.8721 0.6076
#> bloco      2 0.08263 0.041316  2.1409 0.1331
#> Residuals 34 0.65615 0.019298

#> # A tibble: 2 × 5
#>   designacao bloco   pnc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          8     1 0.662  3.65 0.318
#> 2         10     1 0.811  5.39 0.371

#> 
#> NULL
#> [1] "========================"
#> [1] "nrt"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value  Pr(>F)   
#> trat      17 95.367  5.6098  2.7879 0.00534 **
#> bloco      2  3.586  1.7928  0.8910 0.41961   
#> Residuals 34 68.414  2.0122                   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco   nrt    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          3     1   4.5 -3.10  7.62
#> 2          3     3  11.5  4.63  7.38

#> 
#> NULL
#> [1] "========================"
#> [1] "nrc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 63.260  3.7212  4.4458 0.0001065 ***
#> bloco      2  4.333  2.1667  2.5886 0.0898631 .  
#> Residuals 34 28.458  0.8370                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nrnc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value Pr(>F)
#> trat      17  9.2176 0.54221  0.9848 0.4957
#> bloco      2  1.5301 0.76505  1.3895 0.2630
#> Residuals 34 18.7199 0.55059

#> # A tibble: 1 × 5
#>   designacao bloco  nrnc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          3     3  3.75  5.02  1.49

#> 
#> NULL
#> [1] "========================"
#> [1] "msr_100_g"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq    Mean Sq F value    Pr(>F)    
#> trat      17 0.00054259 3.1917e-05  7.5613 3.351e-07 ***
#> bloco      2 0.00007315 3.6574e-05  8.6645 0.0009098 ***
#> Residuals 34 0.00014352 4.2210e-06                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco msr_100_g    rs     yp
#>        <dbl> <dbl>     <dbl> <dbl>  <dbl>
#> 1         13     1     0.025 -3.20 0.0296

#> 
#> NULL
#> [1] "========================"
#> [1] "mr"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.66274 0.038985  4.2389 0.0001675 ***
#> bloco      2 0.01085 0.005427  0.5901 0.5598679    
#> Residuals 34 0.31270 0.009197                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco    mr    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     1 0.841  5.21 0.543
#> 2         14     3 0.236 -4.53 0.511

#> 
#> NULL
#> [1] "========================"
#> [1] "mrc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value   Pr(>F)    
#> trat      17 0.79913 0.047007  5.1261 2.58e-05 ***
#> bloco      2 0.02062 0.010309  1.1241   0.3367    
#> Residuals 34 0.31179 0.009170                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco   mrc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     1 0.908  6.32 0.580
#> 2         14     3 0.275 -4.25 0.539

#> 
#> NULL
#> [1] "========================"
#> [1] "tms_percent"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 542.59  31.917  7.5613 3.351e-07 ***
#> bloco      2  73.15  36.574  8.6645 0.0009098 ***
#> Residuals 34 143.52   4.221                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco tms_percent    rs    yp
#>        <dbl> <dbl>       <dbl> <dbl> <dbl>
#> 1         13     1          25 -3.20  29.6

#> 
#> NULL
#> [1] "========================"
#> [1] "ptms"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.49376 0.029045  3.5783 0.0007603 ***
#> bloco      2 0.07580 0.037902  4.6695 0.0161506 *  
#> Residuals 34 0.27598 0.008117                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco  ptms    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     3 0.106 -3.00 0.299

#> 
#> NULL
#> [1] "========================"
#> [1] "ptms_kg_ha"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 44276548 2604503  3.5783 0.0007603 ***
#> bloco      2  6797527 3398764  4.6695 0.0161506 *  
#> Residuals 34 24747226  727860                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco ptms_kg_ha    rs    yp
#>        <dbl> <dbl>      <dbl> <dbl> <dbl>
#> 1         14     3      1006. -3.00 2835.

#> 
#> NULL
#> [1] "========================"
#> [1] "mfpa_kg_pl"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 17.1811 1.01065  2.8833 0.004188 **
#> bloco      2  0.2468 0.12340  0.3521 0.705774   
#> Residuals 34 11.9176 0.35052                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco mfpa_kg_pl    rs    yp
#>        <dbl> <dbl>      <dbl> <dbl> <dbl>
#> 1         10     2       2.75  3.96  1.20
#> 2         14     1       2.87  3.33  1.50

#> 
#> NULL
#> [1] "========================"
#> [1] "mspa_1000_g"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq    Mean Sq F value   Pr(>F)    
#> trat      17 0.0189000 0.00111176  4.7764 5.28e-05 ***
#> bloco      2 0.0034194 0.00170972  7.3454 0.002231 ** 
#> Residuals 34 0.0079139 0.00023276                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco mspa_1000_g    rs    yp
#>        <dbl> <dbl>       <dbl> <dbl> <dbl>
#> 1         16     1        0.16  3.11 0.126
#> 2         16     3        0.06 -5.23 0.108

#> 
#> NULL
#> [1] "========================"
#> [1] "tmspa_percent"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)    
#> trat      17 189.000 11.1176  4.7764 5.28e-05 ***
#> bloco      2  34.194 17.0972  7.3454 0.002231 ** 
#> Residuals 34  79.139  2.3276                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco tmspa_percent    rs    yp
#>        <dbl> <dbl>         <dbl> <dbl> <dbl>
#> 1         16     1            16  3.11  12.6
#> 2         16     3             6 -5.23  10.7

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Análise exploratória - Multiespectral

Carregando pacotes e banco de dados

data_set <- read_rds("../data/batata-doce-multiespectral-i.rds") |> 
  drop_na()
lista_variaveis <- data_set |> select(r:vari) |> names()
epocas <- data_set |> pull(epoca) |> unique()
# map(lista_variaveis, ~{
#   data_set |> 
#     ggplot(aes(x=!!sym(.x), y = ..density..)) +
#     geom_histogram(color="black",fill="gray", bins = 15) +
#     labs(title = .x) +
#     theme_bw()
# })

Análise de resíduos - Pré-supostos da ANOVA

for(i in seq_along(lista_variaveis)){
  for(j in seq_along(epocas)){
    print("========================")
    print(paste(lista_variaveis[i]," Época: ",epocas[j]))
    print("========================")
    y <- data_set |> filter(epoca == epocas[j]) |> pull(lista_variaveis[i])
    trat <- data_set |> filter(epoca ==epocas[j])|> pull(designacao) |> as_factor()
    bloco <- data_set |> filter(epoca == epocas[j]) |> pull(rep) |> as_factor()
    mod <- aov(y ~ trat + bloco)
    print(anova(mod))
    rs <- rstudent(mod)
    yp <- predict(mod)
    sw_test <- shapiro.test(rs)
    sw_test <- round(sw_test$p.value,5)
    print(
      as_tibble(rs) |> 
        ggplot(aes(rs)) +
        geom_histogram(bins=14,color="black",fill="aquamarine4") +
        labs(title = paste(lista_variaveis[i]," Época: ",epocas[j]),
             subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
        theme_bw()
    )
    df_aux <- data_set |> 
      filter(epoca == epocas[j]) |> 
      select(designacao, rep,lista_variaveis[i]) |> 
      add_column(rs,yp)  |>
      filter(rs > 3 | rs < -3)
    # arrange(rs)
    if(nrow(df_aux) != 0) print(df_aux)
    levene_teste <- lawstat::levene.test(y,trat)
    levene_teste <- round(levene_teste$p.value,5)
    box_plot <- data_set |> 
      filter(epoca == epocas[j]) |> 
      group_by(designacao) |> 
      mutate(
        y_mean = median(!!sym(lista_variaveis[i]),na.rm=TRUE),
        designacao = as_factor(designacao))  |>
      ungroup() |> 
      mutate(designacao = designacao |>  fct_reorder(y_mean)) |> 
      ggplot(aes(x=as_factor(designacao),y=!!sym(lista_variaveis[i]),
                 fill=as_factor(designacao))) +
      geom_boxplot() +
      scale_fill_viridis_d(option = "magma") +
      theme_bw()+
      labs(x="Designacao",
           title =  paste("Levene test - p-valor: ",levene_teste))
    
    print(
      box_plot
    )
    print(cat("\n"))
  }
}
#> [1] "========================"
#> [1] "r  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17 5644685  332040  1.3616 0.21972  
#> bloco      2 2133339 1066670  4.3740 0.02093 *
#> Residuals 32 7803711  243866                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value Pr(>F)
#> trat      17 1439578   84681  0.9109 0.5682
#> bloco      2  315163  157581  1.6951 0.1987
#> Residuals 34 3160685   92961

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value Pr(>F)
#> trat      17 1364421   80260  0.8461 0.6342
#> bloco      2  237444  118722  1.2515 0.2989
#> Residuals 34 3225369   94864

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 2429885  142934   1.299 0.250936   
#> bloco      2 1615715  807857   7.342 0.002237 **
#> Residuals 34 3741109  110033                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 2097094  123358  0.9963 0.484794   
#> bloco      2 1977210  988605  7.9841 0.001437 **
#> Residuals 34 4209942  123822                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 23256775  1368046  0.9206 0.5584501    
#> bloco      2 28795655 14397827  9.6888 0.0004678 ***
#> Residuals 34 50525171  1486034                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17  4031480  237146  1.4174    0.1923    
#> bloco      2 14932896 7466448 44.6270 5.537e-10 ***
#> Residuals 32  5353849  167308                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 5507135  323949  2.7326 0.006153 **
#> bloco      2  949713  474856  4.0055 0.027414 * 
#> Residuals 34 4030759  118552                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep      g    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1          7     2 20041. -3.19 20814.

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 5074978  298528  3.5848 0.0007487 ***
#> bloco      2 1378659  689330  8.2776 0.0011779 ** 
#> Residuals 34 2831392   83276                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 6908059  406356  3.7134 0.0005532 ***
#> bloco      2  882397  441198  4.0318 0.0268375 *  
#> Residuals 34 3720632  109430                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17  4649496  273500  0.9023 0.57681  
#> bloco      2  1935305  967652  3.1925 0.05363 .
#> Residuals 34 10305446  303101                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value   Pr(>F)    
#> trat      17  15691067   923004  0.9012    0.578    
#> bloco      2 107290445 53645223 52.3770 4.14e-11 ***
#> Residuals 34  34823285  1024214                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 1806.96  106.29  2.2799 0.021638 * 
#> bloco      2  686.75  343.38  7.3653 0.002338 **
#> Residuals 32 1491.86   46.62                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep     b    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         11     1  179.  3.03  164.

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 1635.55   96.21  4.2208 0.0001743 ***
#> bloco      2  931.43  465.71 20.4313 1.488e-06 ***
#> Residuals 34  775.00   22.79                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17 4450.8  261.81  0.9721 0.50778  
#> bloco      2 1792.2  896.11  3.3274 0.04789 *
#> Residuals 34 9156.7  269.32                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep     b    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          1     1  255  20.0   182.
#> 2          1     3  133. -3.30  171.

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 1743.39  102.55  3.6111 0.0007036 ***
#> bloco      2 1007.60  503.80 17.7397 5.292e-06 ***
#> Residuals 34  965.58   28.40                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 1740.2  102.36  3.2805  0.001557 ** 
#> bloco      2 1279.8  639.91 20.5073 1.438e-06 ***
#> Residuals 34 1060.9   31.20                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value Pr(>F)
#> trat      17 500.22  29.425  1.2428 0.2861
#> bloco      2 105.47  52.736  2.2274 0.1233
#> Residuals 34 804.96  23.675

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17  96100339  5652961  3.1006  0.002813 ** 
#> bloco      2 166422341 83211171 45.6408 4.247e-10 ***
#> Residuals 32  58341655  1823177                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 163621353  9624785  4.5347 8.800e-05 ***
#> bloco      2  89810311 44905156 21.1569 1.074e-06 ***
#> Residuals 34  72164324  2122480                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 185793828 10929049  6.4649 2.087e-06 ***
#> bloco      2 163848396 81924198 48.4609 1.112e-10 ***
#> Residuals 34  57477764  1690522                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value   Pr(>F)    
#> trat      17 228925047 13466179  5.0962 2.74e-05 ***
#> bloco      2 146168768 73084384 27.6586 7.40e-08 ***
#> Residuals 34  89840767  2642376                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 814049890 47885288  4.8437 4.591e-05 ***
#> bloco      2 138943217 69471608  7.0272   0.00279 ** 
#> Residuals 34 336125270  9886037                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    nir    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1         14     3 22574. -3.17 29600.

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq   Mean Sq F value   Pr(>F)    
#> trat      17 1374181861  80834227  1.0736 0.414928    
#> bloco      2 1351730821 675865411  8.9764 0.000741 ***
#> Residuals 34 2559986050  75293707                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    nir    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1          9     2 20823. -3.29 40759.

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17  5128129   301655  1.3819    0.2094    
#> bloco      2 22011269 11005634 50.4185 1.286e-10 ***
#> Residuals 32  6985141   218286                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17  6932745   407809  0.7202    0.7615    
#> bloco      2 23454808 11727404 20.7103 1.312e-06 ***
#> Residuals 34 19252830   566260                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 11736032   690355  0.8209    0.6601    
#> bloco      2 48222823 24111412 28.6716 5.054e-08 ***
#> Residuals 34 28592381   840952                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep red_edge    rs     yp
#>        <dbl> <dbl>    <dbl> <dbl>  <dbl>
#> 1          9     2   23300. -3.31 25418.

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17  3887897   228700  0.3232    0.9921    
#> bloco      2 36270352 18135176 25.6255 1.634e-07 ***
#> Residuals 34 24061775   707699                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq  F value    Pr(>F)    
#> trat      17  24549652  1444097   2.5702  0.009357 ** 
#> bloco      2 160975995 80487998 143.2550 < 2.2e-16 ***
#> Residuals 34  19102938   561851                       
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17  88487496   5205147  1.2265     0.297    
#> bloco      2 330602479 165301240 38.9496 1.604e-09 ***
#> Residuals 34 144295100   4243974                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.056511 0.0033242  2.1743   0.02836 *  
#> bloco      2 0.059519 0.0297597 19.4650 2.945e-06 ***
#> Residuals 32 0.048924 0.0015289                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep    ndvi    rs      yp
#>        <dbl> <dbl>   <dbl> <dbl>   <dbl>
#> 1         16     1 -0.0764 -3.44  0.0154
#> 2         16     3  0.0873  5.85 -0.0397

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.077870 0.0045806  4.8579 4.458e-05 ***
#> bloco      2 0.054021 0.0270107 28.6457 5.103e-08 ***
#> Residuals 34 0.032059 0.0009429                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.060480 0.0035577  4.9381 3.780e-05 ***
#> bloco      2 0.061047 0.0305236 42.3670 5.853e-10 ***
#> Residuals 34 0.024496 0.0007205                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.088152 0.005185  4.5149 9.182e-05 ***
#> bloco      2 0.064243 0.032121 27.9676 6.582e-08 ***
#> Residuals 34 0.039050 0.001149                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.199961 0.011762  4.5566 8.397e-05 ***
#> bloco      2 0.073812 0.036906 14.2970 3.120e-05 ***
#> Residuals 34 0.087767 0.002581                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   ndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         14     3 0.0328 -3.20 0.147

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.122200 0.007188  0.8513 0.62890  
#> bloco      2 0.068734 0.034367  4.0698 0.02603 *
#> Residuals 34 0.287106 0.008444                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   ndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          9     2 0.0938 -3.12 0.297

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.040335 0.0023727  3.1045  0.002787 ** 
#> bloco      2 0.035372 0.0176861 23.1415 6.077e-07 ***
#> Residuals 32 0.024456 0.0007643                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.062517 0.0036775  4.2166 0.0001759 ***
#> bloco      2 0.029763 0.0148814 17.0630 7.393e-06 ***
#> Residuals 34 0.029653 0.0008721                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep gndvi    rs     yp
#>        <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1          7     2 0.145  3.12 0.0802

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.061623 0.0036249  5.1945 2.250e-05 ***
#> bloco      2 0.062059 0.0310297 44.4658 3.242e-10 ***
#> Residuals 34 0.023726 0.0006978                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.090934 0.0053490  4.5122 9.235e-05 ***
#> bloco      2 0.050324 0.0251618 21.2254 1.041e-06 ***
#> Residuals 34 0.040306 0.0011855                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.183034 0.0107667  3.9898 0.0002926 ***
#> bloco      2 0.046750 0.0233750  8.6620 0.0009113 ***
#> Residuals 34 0.091751 0.0026986                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  gndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         14     3 0.0324 -3.23 0.150

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.19450 0.011441  1.0122 0.46985  
#> bloco      2 0.09261 0.046304  4.0967 0.02547 *
#> Residuals 34 0.38429 0.011303                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  gndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          9     2 0.0158 -3.05 0.247

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.125309 0.007371  4.1087 0.0002837 ***
#> bloco      2 0.159384 0.079692 44.4206 5.847e-10 ***
#> Residuals 32 0.057409 0.001794                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.174173 0.010245  4.8002 5.025e-05 ***
#> bloco      2 0.125200 0.062600 29.3294 3.963e-08 ***
#> Residuals 34 0.072569 0.002134                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.137322 0.008078  4.9156 3.958e-05 ***
#> bloco      2 0.138688 0.069344 42.1983 6.143e-10 ***
#> Residuals 34 0.055872 0.001643                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.19834 0.011667  4.5149 9.182e-05 ***
#> bloco      2 0.14454 0.072271 27.9676 6.582e-08 ***
#> Residuals 34 0.08786 0.002584                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.44990 0.026465  4.5566 8.397e-05 ***
#> bloco      2 0.16607 0.083037 14.2969 3.120e-05 ***
#> Residuals 34 0.19747 0.005808                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   savi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         14     3 0.0492 -3.20 0.221

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.27495 0.016173  0.8513 0.62890  
#> bloco      2 0.15465 0.077325  4.0699 0.02602 *
#> Residuals 34 0.64598 0.018999                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  savi    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          9     2 0.141 -3.12 0.445

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.074564 0.004386  4.0407 0.0003286 ***
#> bloco      2 0.093156 0.046578 42.9096 8.769e-10 ***
#> Residuals 32 0.034736 0.001085                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.104781 0.006164  4.8579 4.458e-05 ***
#> bloco      2 0.072690 0.036345 28.6457 5.103e-08 ***
#> Residuals 34 0.043139 0.001269                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.081381 0.004787  4.9381 3.780e-05 ***
#> bloco      2 0.082144 0.041072 42.3670 5.853e-10 ***
#> Residuals 34 0.032961 0.000969                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.122858 0.007227  4.3643 0.0001272 ***
#> bloco      2 0.089630 0.044815 27.0633 9.296e-08 ***
#> Residuals 34 0.056302 0.001656                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.269066 0.015827  4.5566 8.397e-05 ***
#> bloco      2 0.099321 0.049660 14.2969 3.120e-05 ***
#> Residuals 34 0.118099 0.003473                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  osavi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         14     3 0.0380 -3.20 0.171

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.15415 0.009068  0.7427 0.73936  
#> bloco      2 0.07608 0.038039  3.1157 0.05722 .
#> Residuals 34 0.41510 0.012209                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.032583 0.0019166  3.0572  0.003123 ** 
#> bloco      2 0.028568 0.0142839 22.7838 7.039e-07 ***
#> Residuals 32 0.020062 0.0006269                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.060707 0.0035710  3.8834 0.0003730 ***
#> bloco      2 0.016138 0.0080688  8.7747 0.0008459 ***
#> Residuals 34 0.031265 0.0009195                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.062260 0.0036623  3.7593 0.0004970 ***
#> bloco      2 0.023093 0.0115464 11.8522 0.0001243 ***
#> Residuals 34 0.033123 0.0009742                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.079043 0.0046496  4.0802 0.0002385 ***
#> bloco      2 0.011771 0.0058855  5.1647 0.0109996 *  
#> Residuals 34 0.038745 0.0011396                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.244314 0.0143714  4.6363 7.088e-05 ***
#> bloco      2 0.017213 0.0086066  2.7766   0.07639 .  
#> Residuals 34 0.105391 0.0030997                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.17140 0.010082  0.9849    0.4956    
#> bloco      2 0.31750 0.158752 15.5072 1.637e-05 ***
#> Residuals 34 0.34807 0.010237                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   ndre    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          9     2 -0.137 -3.47 0.105

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq   Mean Sq F value  Pr(>F)    
#> trat      17 0.0046704 0.0002747  2.0921 0.03501 *  
#> bloco      2 0.0064776 0.0032388 24.6644 3.3e-07 ***
#> Residuals 32 0.0042021 0.0001313                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  2"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.0043419 2.554e-04  2.7398  0.006041 ** 
#> bloco      2 0.0034920 1.746e-03 18.7299 3.282e-06 ***
#> Residuals 34 0.0031695 9.322e-05                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq    Mean Sq F value  Pr(>F)  
#> trat      17 0.00252591 1.4858e-04  1.9108 0.05305 .
#> bloco      2 0.00012706 6.3531e-05  0.8170 0.45023  
#> Residuals 34 0.00264386 7.7761e-05                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq    Mean Sq F value   Pr(>F)   
#> trat      17 0.0027279 0.00016046  1.3645 0.214575   
#> bloco      2 0.0017842 0.00089211  7.5861 0.001888 **
#> Residuals 34 0.0039983 0.00011760                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   vari    rs       yp
#>        <dbl> <dbl>  <dbl> <dbl>    <dbl>
#> 1          4     3 0.0202  3.27 -0.00459

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  6"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value Pr(>F)
#> trat      17 0.047623 0.0028014  1.1834 0.3274
#> bloco      2 0.008328 0.0041642  1.7591 0.1875
#> Residuals 34 0.080487 0.0023673

#> # A tibble: 3 × 5
#>   designacao   rep    vari    rs      yp
#>        <dbl> <dbl>   <dbl> <dbl>   <dbl>
#> 1          3     2 -0.177  -3.20 -0.0677
#> 2         12     1 -0.0399 -3.65  0.0809
#> 3         12     3  0.293   7.84  0.111

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  7"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.017385 0.0010226  1.0832    0.4067    
#> bloco      2 0.034355 0.0171774 18.1946 4.242e-06 ***
#> Residuals 34 0.032099 0.0009441                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL

Análise exploratória - Batata Doce - VERÃO

Carregando pacotes e banco de dados

data_set <- read_rds("../data/batata-doce-ednaldo-v.rds")
lista_variaveis <- data_set |> select(pt:tmspa_percent) |> names()
# map(lista_variaveis, ~{
#   data_set |> 
#     ggplot(aes(x=!!sym(.x), y = ..density..)) +
#     geom_histogram(color="black",fill="gray", bins = 15) +
#     labs(title = .x) +
#     theme_bw()
# })

Análise de resíduos - Pré-supostos da ANOVA

map(lista_variaveis, ~{
  print("========================")
  print(.x)
  print("========================")
  y <- data_set |> pull(!!sym(.x))
  trat <- data_set |> pull(designacao) |> as_factor()
  bloco <- data_set |> pull(bloco) |> as_factor()
  mod <- aov(y ~ trat + bloco)
  print(anova(mod))
  rs <- rstudent(mod)
  yp <- predict(mod)
  sw_test <- shapiro.test(rs)
  sw_test <- round(sw_test$p.value,5)
  print(
    as_tibble(rs) |> 
      ggplot(aes(rs)) +
      geom_histogram(bins=14,color="black",fill="aquamarine4") +
      labs(title = .x,
           subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
      theme_bw()
  )
  df_aux <- data_set |> 
    select(designacao, bloco,!!sym(.x)) |> 
    add_column(rs,yp)  |> 
    filter(rs > 3 | rs < -3)
  # arrange(rs)
  if(nrow(df_aux) != 0) print(df_aux)
  levene_teste <- lawstat::levene.test(y,trat)
  levene_teste <- round(levene_teste$p.value,5)
  box_plot <- data_set |> 
    group_by(designacao) |> 
    mutate(
      y_mean = median(!!sym(.x),na.rm=TRUE),
      designacao = as_factor(designacao))  |>
    ungroup() |> 
    mutate(designacao = designacao |>  fct_reorder(y_mean)) |> 
    ggplot(aes(x=as_factor(designacao),y=!!sym(.x),
               fill=as_factor(designacao))) +
    geom_boxplot() +
    scale_fill_viridis_d(option = "magma") +
    theme_bw()+
    labs(x="Designacao",
         title =  paste("Levene test - p-valor: ",levene_teste))
  
  print(
    box_plot
  )
  print(cat("\n"))
})
#> [1] "========================"
#> [1] "pt"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 7.1664 0.42155  5.0518 2.998e-05 ***
#> bloco      2 0.4789 0.23944  2.8694   0.07055 .  
#> Residuals 34 2.8372 0.08345                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "pc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 7.1686 0.42168  5.0409 3.064e-05 ***
#> bloco      2 0.4867 0.24336  2.9092   0.06819 .  
#> Residuals 34 2.8441 0.08365                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "pnc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq    Mean Sq F value    Pr(>F)    
#> trat      17 0.0111922 0.00065836  3.8580 0.0003955 ***
#> bloco      2 0.0003301 0.00016503  0.9671 0.3904093    
#> Residuals 34 0.0058021 0.00017065                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nrt"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 103.250  6.0735 14.1370 9.351e-11 ***
#> bloco      2   1.774  0.8870  2.0646    0.1425    
#> Residuals 34  14.607  0.4296                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nrc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 74.941  4.4083 14.1773 8.985e-11 ***
#> bloco      2  1.559  0.7794  2.5064   0.09652 .  
#> Residuals 34 10.572  0.3109                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nrnc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 6.7508 0.39711  5.9425 5.349e-06 ***
#> bloco      2 0.0849 0.04246  0.6353    0.5359    
#> Residuals 34 2.2721 0.06683                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco  nrnc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         16     3 0.333 -3.41 0.944

#> 
#> NULL
#> [1] "========================"
#> [1] "msr_100_g"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq    Mean Sq F value   Pr(>F)    
#> trat      17 0.00110824 6.5191e-05  9.7436 1.42e-08 ***
#> bloco      2 0.00000215 1.0740e-06  0.1605   0.8524    
#> Residuals 34 0.00022748 6.6910e-06                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "mr"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value   Pr(>F)   
#> trat      17 0.45046 0.026497  2.6384 0.007842 **
#> bloco      2 0.06187 0.030936  3.0803 0.058956 . 
#> Residuals 34 0.34146 0.010043                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "mrc"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.50393 0.029643  2.0082 0.04102 *
#> bloco      2 0.07618 0.038088  2.5804 0.09051 .
#> Residuals 34 0.50187 0.014761                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao bloco   mrc    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1          6     1 0.296 -3.06 0.560
#> 2         17     1 0.509  3.15 0.239

#> 
#> NULL
#> [1] "========================"
#> [1] "tms_percent"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)    
#> trat      17 1108.24  65.191  9.7436 1.42e-08 ***
#> bloco      2    2.15   1.074  0.1605   0.8524    
#> Residuals 34  227.48   6.691                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ptms"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq   Mean Sq F value   Pr(>F)    
#> trat      17 0.41544 0.0244378  4.6263 7.24e-05 ***
#> bloco      2 0.02957 0.0147836  2.7987  0.07496 .  
#> Residuals 34 0.17960 0.0052824                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ptms_kg_ha"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value   Pr(>F)    
#> trat      17 16557052  973944  4.6263 7.24e-05 ***
#> bloco      2  1178370  589185  2.7987  0.07496 .  
#> Residuals 34  7157793  210523                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "mfpa_kg"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 9.6992 0.57054  4.4305 0.0001101 ***
#> bloco      2 0.7243 0.36214  2.8121 0.0740963 .  
#> Residuals 34 4.3784 0.12878                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao bloco mfpa_kg    rs    yp
#>        <dbl> <dbl>   <dbl> <dbl> <dbl>
#> 1         17     2    3.01  3.14  2.21

#> 
#> NULL
#> [1] "========================"
#> [1] "mspa_1000_g"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq    Mean Sq F value    Pr(>F)    
#> trat      17 0.0066079 0.00038870  1.4289 0.1833659    
#> bloco      2 0.0052343 0.00261713  9.6207 0.0004885 ***
#> Residuals 34 0.0092491 0.00027203                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "tmspa_percent"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 66.079  3.8870  1.4289 0.1833659    
#> bloco      2 52.343 26.1713  9.6207 0.0004885 ***
#> Residuals 34 92.491  2.7203                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

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#> NULL
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#> NULL

Análise exploratória - Multiespectral

Carregando pacotes e banco de dados

data_set <- read_rds("../data/batata-doce-multiespectral-v.rds")
lista_variaveis <- data_set |> select(r:vari) |> names()
epocas <- data_set |> pull(epoca) |> unique()
# map(lista_variaveis, ~{
#   data_set |> 
#     ggplot(aes(x=!!sym(.x), y = ..density..)) +
#     geom_histogram(color="black",fill="gray", bins = 15) +
#     labs(title = .x) +
#     theme_bw()
# })

Análise de resíduos - Pré-supostos da ANOVA

for(i in seq_along(lista_variaveis)){
  for(j in seq_along(epocas)){
    print("========================")
    print(paste(lista_variaveis[i]," Época: ",epocas[j]))
    print("========================")
    y <- data_set |> filter(epoca == epocas[j]) |> pull(lista_variaveis[i])
    trat <- data_set |> filter(epoca ==epocas[j])|> pull(designacao) |> as_factor()
    bloco <- data_set |> filter(epoca == epocas[j]) |> pull(rep) |> as_factor()
    mod <- aov(y ~ trat + bloco)
    print(anova(mod))
    rs <- rstudent(mod)
    yp <- predict(mod)
    sw_test <- shapiro.test(rs)
    sw_test <- round(sw_test$p.value,5)
    print(
      as_tibble(rs) |> 
        ggplot(aes(rs)) +
        geom_histogram(bins=14,color="black",fill="aquamarine4") +
        labs(title = paste(lista_variaveis[i]," Época: ",epocas[j]),
             subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
        theme_bw()
    )
    df_aux <- data_set |> 
      filter(epoca == epocas[j]) |> 
      select(designacao, rep,lista_variaveis[i]) |> 
      add_column(rs,yp)  |> 
      filter(rs > 3 | rs < -3)
    # arrange(rs)
    if(nrow(df_aux) != 0) print(df_aux)
    levene_teste <- lawstat::levene.test(y,trat)
    levene_teste <- round(levene_teste$p.value,5)
    box_plot <- data_set |> 
      filter(epoca == epocas[j]) |> 
      group_by(designacao) |> 
      mutate(
        y_mean = median(!!sym(lista_variaveis[i]),na.rm=TRUE),
        designacao = as_factor(designacao))  |>
      ungroup() |> 
      mutate(designacao = designacao |>  fct_reorder(y_mean)) |> 
      ggplot(aes(x=as_factor(designacao),y=!!sym(lista_variaveis[i]),
                 fill=as_factor(designacao))) +
      geom_boxplot() +
      scale_fill_viridis_d(option = "magma") +
      theme_bw()+
      labs(x="Designacao",
           title =  paste("Levene test - p-valor: ",levene_teste))
    
    print(
      box_plot
    )
    print(cat("\n"))
  }
}
#> [1] "========================"
#> [1] "r  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value  Pr(>F)   
#> trat      17 2057358  121021  0.9087 0.57039   
#> bloco      2 1509206  754603  5.6660 0.00752 **
#> Residuals 34 4528110  133180                   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value Pr(>F)
#> trat      17 25991312 1528901  0.9059 0.5732
#> bloco      2   424308  212154  0.1257 0.8823
#> Residuals 34 57383904 1687762

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17 16715382  983258  0.6756 0.80376  
#> bloco      2 12998996 6499498  4.4655 0.01897 *
#> Residuals 34 49486177 1455476                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep      r    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1         11     3 17365. -3.49 20264.
#> 2         17     1 15763. -3.40 18608.

#> 
#> NULL
#> [1] "========================"
#> [1] "r  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17 8805500  517971  2.2145 0.02377 *
#> bloco      2  680080  340040  1.4538 0.24784  
#> Residuals 34 7952509  233897                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep     r    rs     yp
#>        <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1         14     3 18611 -3.87 19862.

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 419963521 24703737  4.6480 6.915e-05 ***
#> bloco      2  44315094 22157547  4.1689   0.02403 *  
#> Residuals 34 180706732  5314904                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 230547651 13561627  0.7708 0.7112
#> bloco      2  74577442 37288721  2.1194 0.1357
#> Residuals 34 598189176 17593799

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 214544068  12620239  2.2931   0.01933 *  
#> bloco      2 634036677 317018338 57.6010 1.205e-11 ***
#> Residuals 34 187125473   5503690                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep      g    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1         12     1 20955. -3.74 26877.

#> 
#> NULL
#> [1] "========================"
#> [1] "g  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 120513568  7089033  7.1277 6.757e-07 ***
#> bloco      2  54704500 27352250 27.5013 7.858e-08 ***
#> Residuals 34  33815675   994579                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 2240.05  131.77  1.8717 0.058792 . 
#> bloco      2  841.87  420.94  5.9793 0.005955 **
#> Residuals 34 2393.56   70.40                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value   Pr(>F)   
#> trat      17 1410.71  82.983  2.4141 0.014061 * 
#> bloco      2  477.33 238.663  6.9431 0.002962 **
#> Residuals 34 1168.71  34.374                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value Pr(>F)
#> trat      17  437.07  25.710  0.8548 0.6253
#> bloco      2   32.39  16.192  0.5383 0.5886
#> Residuals 34 1022.66  30.078

#> 
#> NULL
#> [1] "========================"
#> [1] "b  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
#> trat      17 164.538  9.6787  1.6897 0.09462 .
#> bloco      2  37.261 18.6304  3.2525 0.05099 .
#> Residuals 34 194.755  5.7281                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq   Mean Sq F value  Pr(>F)  
#> trat      17 2002243348 117779020  1.7418 0.08264 .
#> bloco      2  697728463 348864231  5.1592 0.01105 *
#> Residuals 34 2299071503  67619750                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq   Mean Sq F value Pr(>F)
#> trat      17 4.2379e+09 249290192  0.7803 0.7016
#> bloco      2 1.1116e+09 555778907  1.7397 0.1908
#> Residuals 34 1.0862e+10 319471672

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df     Sum Sq    Mean Sq F value    Pr(>F)    
#> trat      17 1615335174   95019716  0.8012    0.6803    
#> bloco      2 7208514536 3604257268 30.3893 2.698e-08 ***
#> Residuals 34 4032498947  118602910                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    nir    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1         14     2 22381. -3.34 47697.

#> 
#> NULL
#> [1] "========================"
#> [1] "nir  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 192219117 11307007  3.5008 0.0009145 ***
#> bloco      2 137588642 68794321 21.2994 1.008e-06 ***
#> Residuals 34 109815848  3229878                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    nir    rs     yp
#>        <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1         14     1 50314.  4.91 44912.

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 192002996 11294294  1.7199 0.0874683 .  
#> bloco      2 154738631 77369316 11.7822 0.0001296 ***
#> Residuals 34 223265913  6566644                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value   Pr(>F)   
#> trat      17  78862732  4638984  1.0838 0.406185   
#> bloco      2  71292735 35646367  8.3280 0.001139 **
#> Residuals 34 145530400  4280306                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value   Pr(>F)   
#> trat      17 47431346  2790079  1.0619 0.425073   
#> bloco      2 35541201 17770601  6.7635 0.003366 **
#> Residuals 34 89332457  2627425                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "red_edge  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17  63827976  3754587  1.0038 0.47767  
#> bloco      2  24359402 12179701  3.2564 0.05082 .
#> Residuals 34 127166875  3740202                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep red_edge    rs     yp
#>        <dbl> <dbl>    <dbl> <dbl>  <dbl>
#> 1         13     1   23867. -3.11 28128.
#> 2         14     3   24213. -3.22 28593.

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.244324 0.014372  1.8589 0.06081 .
#> bloco      2 0.079565 0.039783  5.1455 0.01116 *
#> Residuals 34 0.262872 0.007732                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   ndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          7     2 0.0137 -3.66 0.232

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.60348 0.035499  0.9504 0.5290
#> bloco      2 0.09947 0.049734  1.3314 0.2775
#> Residuals 34 1.27002 0.037354

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 0.12135 0.00714  0.7509    0.7312    
#> bloco      2 0.70167 0.35084 36.9073 3.017e-09 ***
#> Residuals 34 0.32320 0.00951                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "ndvi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df    Sum Sq    Mean Sq F value    Pr(>F)    
#> trat      17 0.0119208 0.00070122  2.2269 0.0230085 *  
#> bloco      2 0.0062025 0.00310124  9.8489 0.0004226 ***
#> Residuals 34 0.0107060 0.00031488                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  ndvi    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     3  0.33 -4.62 0.381

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value   Pr(>F)   
#> trat      17 0.127282 0.0074872  2.2241 0.023184 * 
#> bloco      2 0.039665 0.0198325  5.8912 0.006357 **
#> Residuals 34 0.114460 0.0033665                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  gndvi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          7     2 0.0116 -3.74 0.158

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.40734 0.023961  0.9694 0.5104
#> bloco      2 0.06240 0.031198  1.2622 0.2959
#> Residuals 34 0.84037 0.024717

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value    Pr(>F)    
#> trat      17 0.20303 0.011943  1.1162    0.3792    
#> bloco      2 0.36018 0.180091 16.8316 8.301e-06 ***
#> Residuals 34 0.36378 0.010700                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL
#> [1] "========================"
#> [1] "gndvi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.061134 0.0035961  7.4827 3.797e-07 ***
#> bloco      2 0.000905 0.0004524  0.9413    0.4001    
#> Residuals 34 0.016340 0.0004806                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep gndvi    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     1 0.332  4.13 0.273

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value  Pr(>F)  
#> trat      17 0.60489 0.035582  1.7927 0.07235 .
#> bloco      2 0.12407 0.062033  3.1253 0.05675 .
#> Residuals 34 0.67484 0.019848                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep   savi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          7     2 0.0206 -3.44 0.356
#> 2          9     2 0.727   3.25 0.406

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 1.32159 0.077740  0.8982 0.5810
#> bloco      2 0.21803 0.109017  1.2595 0.2967
#> Residuals 34 2.94282 0.086553

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 0.56635 0.03331  0.9672    0.5126    
#> bloco      2 1.34900 0.67450 19.5821 2.198e-06 ***
#> Residuals 34 1.17112 0.03444                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   savi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         14     2 0.0903 -3.14 0.502

#> 
#> NULL
#> [1] "========================"
#> [1] "savi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.026821 0.0015777  2.2270 0.0230071 *  
#> bloco      2 0.013955 0.0069777  9.8491 0.0004225 ***
#> Residuals 34 0.024088 0.0007085                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  savi    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     3 0.495 -4.62 0.572

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.38755 0.022797  1.4847 0.1596
#> bloco      2 0.04750 0.023751  1.5468 0.2275
#> Residuals 34 0.52205 0.015354

#> # A tibble: 1 × 5
#>   designacao   rep  osavi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1          1     1 0.0358 -4.36 0.383

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.87218 0.051305  1.1303 0.3679
#> bloco      2 0.13411 0.067056  1.4773 0.2425
#> Residuals 34 1.54329 0.045391

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 0.22723 0.01337  0.6085    0.8616    
#> bloco      2 0.82141 0.41070 18.6961 3.335e-06 ***
#> Residuals 34 0.74689 0.02197                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 2 × 5
#>   designacao   rep  osavi    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         11     3 0.156  -3.23 0.492
#> 2         14     2 0.0698 -3.32 0.413

#> 
#> NULL
#> [1] "========================"
#> [1] "osavi  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value  Pr(>F)  
#> trat      17 0.036633 0.0021549  1.4623 0.16880  
#> bloco      2 0.014884 0.0074418  5.0499 0.01201 *
#> Residuals 34 0.050104 0.0014736                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep osavi    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         14     3 0.221 -12.5 0.383

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value   Pr(>F)   
#> trat      17 0.28205 0.016591  1.1881 0.323916   
#> bloco      2 0.17577 0.087884  6.2937 0.004727 **
#> Residuals 34 0.47477 0.013964                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    ndre    rs    yp
#>        <dbl> <dbl>   <dbl> <dbl> <dbl>
#> 1          1     1 -0.0196 -3.66 0.274

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq  Mean Sq F value Pr(>F)
#> trat      17 0.85255 0.050150  1.0579 0.4286
#> bloco      2 0.08050 0.040252  0.8491 0.4367
#> Residuals 34 1.61183 0.047407

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trat      17 0.28487 0.01676  0.5409    0.9106    
#> bloco      2 0.81468 0.40734 13.1499 5.885e-05 ***
#> Residuals 34 1.05321 0.03098                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep   ndre    rs    yp
#>        <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         11     3 -0.180 -3.31 0.227

#> 
#> NULL
#> [1] "========================"
#> [1] "ndre  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.048269 0.0028394  1.1634 0.3422224    
#> bloco      2 0.055013 0.0275064 11.2705 0.0001758 ***
#> Residuals 34 0.082979 0.0024406                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    ndre    rs    yp
#>        <dbl> <dbl>   <dbl> <dbl> <dbl>
#> 1         14     3 -0.0561 -5.96 0.108

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  1"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.088754 0.0052208  3.4681 0.0009889 ***
#> bloco      2 0.008653 0.0043264  2.8739 0.0702758 .  
#> Residuals 34 0.051184 0.0015054                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep    vari    rs     yp
#>        <dbl> <dbl>   <dbl> <dbl>  <dbl>
#> 1          7     2 0.00147 -3.02 0.0850

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  3"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value Pr(>F)
#> trat      17 0.101124 0.0059485  1.0934 0.3981
#> bloco      2 0.016702 0.0083508  1.5350 0.2300
#> Residuals 34 0.184975 0.0054404

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  4"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.086638 0.0050964  3.7624 0.0004934 ***
#> bloco      2 0.061210 0.0306052 22.5946 5.726e-07 ***
#> Residuals 34 0.046054 0.0013545                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> # A tibble: 1 × 5
#>   designacao   rep  vari    rs    yp
#>        <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1         13     1 0.190  3.25 0.106

#> 
#> NULL
#> [1] "========================"
#> [1] "vari  Época:  5"
#> [1] "========================"
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df   Sum Sq   Mean Sq F value    Pr(>F)    
#> trat      17 0.042047 0.0024733  9.3999 2.253e-08 ***
#> bloco      2 0.007475 0.0037376 14.2048 3.280e-05 ***
#> Residuals 34 0.008946 0.0002631                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#> 
#> NULL